Addressing Fairness Issues in Deep Learning-Based Medical Image Analysis: A Systematic Review

TL;DR

This paper systematically reviews recent methods addressing fairness issues in medical image analysis using deep learning, highlighting evaluation techniques, mitigation strategies, and future challenges to promote equitable healthcare AI.

Contribution

It categorizes and analyzes current fairness evaluation and mitigation methods in MedIA, providing a comprehensive framework for future research and development.

Findings

Identification of key fairness evaluation methods

Overview of unfairness mitigation strategies

Discussion of challenges and future opportunities

Abstract

Deep learning algorithms have demonstrated remarkable efficacy in various medical image analysis (MedIA) applications. However, recent research highlights a performance disparity in these algorithms when applied to specific subgroups, such as exhibiting poorer predictive performance in elderly females. Addressing this fairness issue has become a collaborative effort involving AI scientists and clinicians seeking to understand its origins and develop solutions for mitigation within MedIA. In this survey, we thoroughly examine the current advancements in addressing fairness issues in MedIA, focusing on methodological approaches. We introduce the basics of group fairness and subsequently categorize studies on fair MedIA into fairness evaluation and unfairness mitigation. Detailed methods employed in these studies are presented too. Our survey concludes with a discussion of existing challenges and opportunities in establishing a fair MedIA and healthcare system. By offering this comprehensive review, we aim to foster a shared understanding of fairness among AI researchers and clinicians, enhance the development of unfairness mitigation methods, and contribute to the creation of an equitable MedIA society.